Conversational AI agency VERSA is to partner Mogaé in a joint venture. Announcing a Diwali India launch, VERSA said its expansion into this country is to capitalise on demand for specialised conversational strategy and design in a market with a population of more than 1.3 billion people, and an installed base of nearly a billion mobile phones. VERSA India will be a 50/50 joint venture between VERSA (Headquartered in Melbourne, Australia; with US operations out of Seattle) and Mogaé Consultants, owned by Sandeep & Tanya Goyal. Dr. Sandeep Goyal, is a well-known advertising & media veteran who has been a past President of Rediffusion, ex-Group CEO of Zee Telefilms and former Founder Chairman of Dentsu India. Tanya Goyal has been a six-term member of the Governing Council of the Advertising Agencies Association of India (AAAI) and was Executive Director of Dentsu India.

Chameleon tongues, gecko feet and octopus tentacles are just a few of the animal body parts we've seen inspire soft robotic grippers, but nature still has plenty to offer researchers in this field. A team in Australia is the latest to tap into the world of biomimicry, demonstrating a new type of robotic gripper modeled on an elephant's trunk, with the ability to pick up and release objects even when they're tucked away in confined spaces.The invention is the handiwork of a robotics team working under Dr Thanh Nho Do at Australia's University of New South Wales (UNSW), who just a couple of months ago showed off a haptic feedback device that can give gloves a finer sense of touch. This time around the team set out to recreate the gripping abilities of elephants and other animals that are similarly able to embrace objects using their coiling body parts."Animals "These animals can do this because …

Australia Post is also using machine learning to predict mail volumes. "We got it to a point now off that data analytics that we can see plus or minus 5% …

Millimeter-wave (mmW) radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems (ADAS) by enabling robust and high-performance object detection, localization, as well as recognition - a key component of new environmental perception. In this paper, we propose a novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that extracts the location and class of objects based on further processing of the range-velocity-angle (RVA) heatmap sequences. To bypass the complexity of 4D convolutional neural networks (NN), we propose to combine several lower-dimension NN models within our RAMP-CNN model that nonetheless approaches the performance upper-bound with lower complexity. The extensive experiments show that the proposed RAMP-CNN model achieves better average recall (AR) and average precision (AP) than prior works in all testing scenarios (see Table. III). Besides, the RAMP-CNN model is validated to work robustly under the nighttime, which enables low-cost radars as a potential substitute for pure optical sensing under severe conditions.

This paper presents a focused analysis of human studies in explainable artificial intelligence (XAI) entailing qualitative investigation. We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous. We contextualize the presentation of the XAI contributions included in our analysis according to the components of rigor described in the qualitative research literature: 1) underlying theories or frameworks, 2) methodological approaches, 3) data collection methods, and 4) data analysis processes. The results of our analysis support calls from others in the XAI community advocating for collaboration with experts from social disciplines to bolster rigor and effectiveness in human studies.

A team of scientists led by Nanyang Technological University, Singapore (NTU Singapore) has invented an artificial olfactory system that mimics the mammalian nose to assess the freshness of meat accurately. The'electronic nose' (e-nose) comprises a'barcode' that changes colour over time in reaction to the gases produced by meat as it decays, and a barcode'reader' in the form of a smartphone app powered by artificial intelligence (AI). The e-nose has been trained to recognise and predict meat freshness from a large library of barcode colours. When tested on commercially packaged chicken, fish and beef meat samples that were left to age, the team found that their deep convolutional neural network AI algorithm that powers the e-nose predicted the freshness of the meats with a 98.5 per cent accuracy. As a comparison, the research team assessed the prediction accuracy of a commonly used algorithm to measure the response of sensors like the barcode used in this e-nose.

The future of work is an estimated subject. Will we come to acknowledge wearables as consistently on-trackers of our temperament, prosperity, and efficiency? Will virtual reality transform our entire workplace? Will robots be our teammates? In the midst of the perpetual progression of "what if's" and "when's," one previous forecast is presently guaranteed: artificial intelligence is completely installed into the working environment.

Progressive digitalization is changing the game of many industrial sectors. Focus-ing on product quality the main profitability driver of this so-called Industry 4.0 will be the horizontal integration of information over the complete supply chain. Therefore, the European RFCS project 'Quality4.0' aims in developing an adap-tive platform, which releases decisions on product quality and provides tailored information of high reliability that can be individually exchanged with customers. In this context Machine Learning will be used to detect outliers in the quality data. This paper discusses the intermediate project results and the concepts developed so far for this horizontal integration of quality information.

Roof falls due to geological conditions are major safety hazards in mining and tunneling industries, causing lost work times, injuries, and fatalities. Several large-opening limestone mines in the Eastern and Midwestern United States have roof fall problems caused by high horizontal stresses. The typical hazard management approach for this type of roof fall hazard relies heavily on visual inspections and expert knowledge. In this study, we propose an artificial intelligence (AI) based system for the detection roof fall hazards caused by high horizontal stresses. We use images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network for autonomous detection of hazardous roof conditions. To compensate for limited input data, we utilize a transfer learning approach. In transfer learning, an already-trained network is used as a starting point for classification in a similar domain. Results confirm that this approach works well for classifying roof conditions as hazardous or safe, achieving a statistical accuracy of 86%. However, accuracy alone is not enough to ensure a reliable hazard management system. System constraints and reliability are improved when the features being used by the network are understood. Therefore, we used a deep learning interpretation technique called integrated gradients to identify the important geologic features in each image for prediction. The analysis of integrated gradients shows that the system mimics expert judgment on roof fall hazard detection. The system developed in this paper demonstrates the potential of deep learning in geological hazard management to complement human experts, and likely to become an essential part of autonomous tunneling operations in those cases where hazard identification heavily depends on expert knowledge.

Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all existing domain adaptation methods heavily require large amounts of unlabeled target domain data for learning domain invariant representations to achieve good generalizability on the target domain. In fact, in many real-world applications, target domain data may not always be available. In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation. To tackle this problem, under the assumption that causal relationships between features and the class variable are robust across domains, we propose a novel Causal AutoEncoder (CAE), which integrates deep autoencoder and causal structure learning into a unified model to learn causal representations only using data from a single source domain. Specifically, a deep autoencoder model is adopted to learn low-dimensional representations, and a causal structure learning model is designed to separate the low-dimensional representations into two groups: causal representations and task-irrelevant representations. Using three real-world datasets the extensive experiments have validated the effectiveness of CAE compared to eleven state-of-the-art methods.